About me:

I am a PhD candidate in Department of Chemical Engineering at The Pennsylvania State University- University Park. I got my BSc in Chemical Engineering from Sharif University of Technology and my MSc in Chemical Engineering-Biotechnology from AmirKabir University of Technology (Tehran Polytechninc). I joined Penn State, the home of Nittany Lion, in July 2006, and I am currently a member of Chemical and Biological Systems Optimization Lab, working under supervision of Prof. Costas D. Maranas.

Contact Information:

147 Fenske Laboratory

Phone: 

814 863 3385

The Pennsylvania State University-

Fax:      

813 865 7816

University Park, PA, 16802, USA

E-Mail:  

zomorrodi@engr.psu.edu

Education:

PhD     Candidate in Chemical Engineering, The Pennsylvania State University- University Park, 2006- present

M.Sc: Chemical Engineering   AmirKabir University of  Technology (Tehran Polytechnic), Iran, 2005 

B.Sc: Chemical Engineering   Sharif University of  Technology,Iran, 2002

Research Interests:

- Applied Mathematics and Statistics

- Bioreactor Design, Optimization and Simulation

- Transport Phenomena in Biological Systems

- Process/Bioprocess Design, Optimization and Control

Current Research:

Subject: Systems Biology and Synthetic Biology

Advisor: Prof. Costas D. Maranas

Abstract:

          My current research is concerned with analysis and redesign of biological networks and pathways. I use optimization-based approaches (linear, non-linear, mixed integer, dynamic and stochastic) to model biological networks at different levels including gene, protein, metabolic and signaling networks. .

MS Thesis:

 Title: Comparing Computational Methods for Reconstruction of Hidden Regulatory Signals in Biological Networks

 Advisors: Dr Bahram Nasernejad and Prof. Amir Assadi

 Consultant Professor: Dr. Jahanshah Kabudian

Abstract:                 

          The biologists now face with the masses of high dimensional datasets generated from various high-throughput technologies such as DNA microarrays, which are outputs of complex inter-connected biological networks at different levels, driven by a number of hidden regulatory layers. The properly inference of these dynamical regulatory networks from masses of available data, necessitates employment of powerful and efficient computational methodologies based on a global scale, using ideas from system identification. In this thesis we aimed to compare a variety of powerful computational methods for reconstruction of hidden regulatory features in biological networks, including Principal Component Analysis (PCA), Independent Component Analysis (ICA), Kernel Principal Component Analysis (KPCA), Network Component Analysis (NCA), and a Multi-layer Perceptron (MLP) Neural Network. All these approaches were first verified experimentally using the absorbance spectra of a network of seven hemoglobin solutions. This analysis showed the superiority of NCA and MLP to the other methods. We then applied these two approaches to available DNA microarray data, to examine their capability for transcriptome-based determination of multiple transcription factor activities during cell cycle in S. cerevisiae and carbon source transition from glucose to acetate in E. coli. The obtained results for S. cerevisiae showed that activities of most of the recognized cell cycle regulators exhibit a cyclic behavior, while the expression levels of responsible genes do not. Also the key results obtained for E. coli, were either consistent with physiology or verified by using independent measurements. The results of these analyses, reveal the potential capability of both MLP and NCA approaches, to address the problem of extracting hidden regulatory features in complex biological networks.

Keywords: Biological network, Regulation, Regulatory signal, Computational methods, E. coli, S. cerevisiae

BS Thesis:

Title: Comparison of PID Controller Tuning Methods

Advisor: Prof. Mohammad Shahrokhi

Abstract:

          Proportional, Integral and derivative (PID) controllers are the most widely-used controller in the chemical process industries because of their simplicity, robustness and successful practical application. Many tuning methods have been proposed for PID controllers. Our purpose in this study is comparison of these tuning methods for single input single output (SISO) systems using computer simulation. Integral of the absolute value of the error (IAE) has been used as the criterion for comparison. These tuning methods have been implemented for first, second and third order systems with dead time and for two cases of set point tracking and load rejection.

Key Words: PID Controller; Tuning Method; Set Point Tracking; Load Rejection

More information        

Favorite Links:

-         My wife's homepage

-         My mom's homepage

-         Iranian Student Association at Penn State

-         Shirvan City (My hometown)

Takhte-e-Jamshid (Perspolis),

The most famous ancient place of Iran

Visitors since 09/2008

Last updated  August 2008